scholarly journals Gas Concentration Prediction Based on the Measured Data of a Coal Mine Rescue Robot

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Xiliang Ma ◽  
Hua Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Coal Mine ◽  
Prediction Error ◽  
Prediction Method ◽  
Gas Concentration ◽  
Quantum Genetic Algorithm ◽  
Rescue Robot ◽  
Concentration Prediction ◽  
Mine Rescue

The coal mine environment is complex and dangerous after gas accident; then a timely and effective rescue and relief work is necessary. Hence prediction of gas concentration in front of coal mine rescue robot is an important significance to ensure that the coal mine rescue robot carries out the exploration and search and rescue mission. In this paper, a gray neural network is proposed to predict the gas concentration 10 meters in front of the coal mine rescue robot based on the gas concentration, temperature, and wind speed of the current position and 1 meter in front. Subsequently the quantum genetic algorithm optimization gray neural network parameters of the gas concentration prediction method are proposed to get more accurate prediction of the gas concentration in the roadway. Experimental results show that a gray neural network optimized by the quantum genetic algorithm is more accurate for predicting the gas concentration. The overall prediction error is 9.12%, and the largest forecasting error is 11.36%; compared with gray neural network, the gas concentration prediction error increases by 55.23%. This means that the proposed method can better allow the coal mine rescue robot to accurately predict the gas concentration in the coal mine roadway.

scholarly journals Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 161 ◽  
Author(s):  
Tianjun Zhang ◽  
Shuang Song ◽  
Shugang Li ◽  
Li Ma ◽  
Shaobo Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Coal Mine ◽  
Time Series Prediction ◽  
Mean Square ◽  
Gas Concentration ◽  
Concentration Time ◽  
Concentration Time Series ◽  
Concentration Prediction

Effective prediction of gas concentrations and reasonable development of corresponding safety measures have important guiding significance for improving coal mine safety management. In order to improve the accuracy of gas concentration prediction and enhance the applicability of the model, this paper proposes a long short-term memory (LSTM) cyclic neural network prediction method based on actual coal mine production monitoring data to select gas concentration time series with larger samples and longer time spans, including model structural design, model training, model prediction, and model optimization to implement the prediction algorithm. By using the minimum objective function as the optimization goal, the Adam optimization algorithm is used to continuously update the weight of the neural network, and the network layer and batch size are tuned to select the optimal one. The number of layers and batch size are used as parameters of the coal mine gas concentration prediction model. Finally, the optimized LSTM prediction model is called to predict the gas concentration in the next time period. The experiment proves the following: The LSTM gas concentration prediction model uses large data volume sample prediction, more accurate than the bidirectional recurrent neural network (BidirectionRNN) model and the gated recurrent unit (GRU) model. The average mean square error of the prediction model can be reduced to 0.003 and the predicted mean square error can be reduced to 0.015, which has higher reliability in gas concentration time series prediction. The prediction error range is 0.0005–0.04, which has better robustness in gas concentration time series prediction. When predicting the trend of gas concentration time series, the gas concentration at the time inflection point can be better predicted and the mean square error at the inflection point can be reduced to 0.014, which has higher applicability in gas concentration time series prediction.

Application of Optimized BPNN Based on QGA in PID Control of Coal Mine Detection Robots

2012 ◽  
Vol 605-607 ◽  
pp. 1605-1608
Author(s):  
Yang Yang He ◽  
Zhi Gang Niu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Pid Control ◽  
Motor Speed ◽  
Mine Detection ◽  
Function Model ◽  
Quantum Genetic Algorithm ◽  
On Line

This thesis regards TUT-CMDR type coal mine detection robots as the research object and put forward an application of optimized BP neural network based on Quantum Genetic Algorithm in PID Control of motor speed. Transfer function model of speed control system of TUT-CMDR motor was established. Firstly, initial weights and thresholds of BP neural network were optimized by Quantum Genetic Algorithm, and then BP neural network was designed to adjust the parameters of PID on line. Finally, the results show that the algorithm is feasible and superiority.

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

scholarly journals Research on Fan Operation Evaluation and Error State Judgment Relying on Improved Neural Network and Intelligent Computing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042005
Author(s):  
Xueyi Liu ◽  
Junhao Dong ◽  
Guangyu Tu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Performance Prediction ◽  
Optimization Method ◽  
Aerodynamic Performance ◽  
Evaluation Index ◽  
Genetic Optimization ◽  
Quantum Genetic Algorithm

Abstract Fan, as the most commonly used mechanical equipment, is widely used. In order to solve the problem of fan bearing fault diagnosis, this paper analyzes the main factors affecting fan spindle speed and power generation in operation. The input and output parameters of the performance prediction model are determined. The performance prediction model of wind turbine is established by using generalized regression neural network, and the smoothing factor of GRNN is optimized by comparing the prediction accuracy of the model. Based on this model, the sliding data window method is used to calculate the residual evaluation index of wind turbine speed and power in real time. When the evaluation index continuously exceeds the pre-set threshold, the abnormal state of wind turbine can be judged. In order to obtain wind turbine blades with better aerodynamic performance, a blade aerodynamic performance optimization method based on quantum heredity is proposed. The B é zier curve control point is used as the design variable to represent the continuous chord length and torsion angle distribution of the blade, the blade shape optimization model aiming at the maximum power is established, and the quantum genetic algorithm is used to optimize the chord length and torsion angle of the blade under different constraints. The optimization results of quantum genetic algorithm and classical genetic algorithm are compared and analyzed. Under the same parameters and boundary conditions, the proposed blade aerodynamic optimization method based on quantum genetic optimization is better than the classical genetic optimization method, and can obtain better blade aerodynamic shape and higher wind energy capture efficiency. This method makes up for the shortcomings of traditional fault diagnosis methods, improves the recognition rate of fault types and the accuracy of fault diagnosis, and the diagnosis effect is good.

The Coal Mine Flooding Emergency Ability Evaluation Based on GA-WNN

2012 ◽  
Vol 452-453 ◽  
pp. 782-788
Author(s):  
Jin Feng Wang ◽  
Li Jie Feng ◽  
Zhao Hui Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Coal Mine ◽  
Evaluation Model ◽  
Wavelet Neural Network ◽  
Time Frequency ◽  
Model Based ◽  
Time Frequency Localization ◽  
Mine Flooding ◽  
Ability Evaluation

For the coal resources working which are affected by the coal mine flooding seriously, this paper make an analysis on the factors which affect the coal mine flooding emergency ability evaluation model based on GA-WNN is established through the wavelet neural network value which is optimized with genetic algorithm. This model combined the global optimization ability of genetic algorithm with the time-frequency localization of wavelet neural network. This combination can make up for many defects (for example, the neural network structure should be given artificially, the function can got local minimum easily and so on). Therefore, the local mine flooding emergency ability evaluation model based on genetic algorithm and wavelet neural network have higher reliability and calculation ability, and is beneficial to the pre-control management for coal mine flooding rescue.

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